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8 results for “IMU Sensor”
Dataset for Monitoring and Visualizing Stroke Rehabilitation Progress using Wearable Sensors (IMU)
<div> <p>This dataset is associated with a manuscript that is currently under peer review.</p> <p> </p> <p>Article Abstract:</p> <p>Stroke is one of the leading causes of death and disability worldwide, and recovering mobility is an important goal during post-stroke rehabilitation. In this work, we present a study to verify the feasibility of monitoring and visualizing longitudinal stroke gait rehabilitation progress using wearable sensors. Wearable devices such as inertial measurement units (IMUs) are easy-to-use and cost-effective tools for quantifying mobility. However, there is a need for research on longitudinal monitoring of stroke rehabilitation progress with wearables, as well as generating clinically relevant insights using appropriate visualizations. To this aim, we recruited ten stroke patients in their early rehabilitation stage. We collected and analyzed the IMU-derived gait features across two visits, and presented visualizations of the foot movement trajectories as well as the spatio-temporal gait parameters in the average, symmetry, and variation domains to quantify changes in gait. Our visualization and quantification methods are evaluated and validated by clinical experts, and prove to be promising in aiding clinicians to monitor rehabilitation progression.</p> <p> </p> <p>Data description:</p> <p>The dataset consists data from ten stroke patients who completed both visits. The "raw" data folder contains tri-axial acceleration and angular velocity data from the IMUs. In addition, information about the participants such as demographics (e.g., body height and body weight), FAC scores at both visits, and evaluations of gait improvement are documented in the file "participant_info.csv".</p> <p>The “interim” folder contains IMU data that has been manually segmented to remove irrelevant movements before and after each walking session during a visit, based on visual inspection of raw IMU signals. For quality control, the segmented accelerometer and gyroscope data of each sensor were plotted, and the plots were saved in the same folder as the IMU signals. In addition, during the first execution of gait parameter extraction, calculated 3D feet trajectories were cached in the "interim" folder, so that for future executions, the cached trajectories can be loaded directly, reducing the computational efforts for re-calculation. The file "stance_magnitude_thresholds_manual.csv" documents the angular velocity thresholds used to identify stance phases for the gait analysis algorithm for each participant. The threshold values were determined manually by observing the angular velocity signals. </p> <p>The “processed” folder contains stride-by-stride spatio-temporal gait parameters extracted for each of the four walking conditions, and aggregated gait parameters in terms of coefficients of variation and symmetry for all walking conditions for each participant. </p> <p> </p> </div>
A five-sensor IMU-based Parkinson's disease patient and control dataset including three activities of daily living
<p class="MsoNormal">Parkinson's disease is an often-debilitating progressive neurological condition leading to loss of motor control. This dataset contains kinematic sensor data from two groups: one containing 15 patients with Parkinson's disease, and a control group of 19 participants without any known neurological condition. Participant ages ranged from 40–85, 21 were male, and 13 were female. The participants wore five 9-axis Inertial Measurement Units (IMUs) – one on each upper arm, each lower arm, and on their head. They were asked to perform a calibration pose, followed by three activities: making toast, putting on a cardigan, and unlocking and opening a door, with each activity repeated three times. The IMUs recorded time-series acceleration and orientation data from the moment where the participant was instructed to begin the activity (inception of the idea to act), through to the activity's completion. This dataset is planned for use in intent-sensing studies for assistive device control but is also applicable for activity recognition.</p>
A five-sensor IMU-based Parkinson's disease patient and control dataset including three activities of daily living
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LiDAR-IMU Mapping Test with Various Range Sensors
<p>This is a set of LiDAR-IMU mapping test sequences recorded with various range sensors.</p> <p>- Ouster OS0-32 & OS0-64</p> <p>- Livox Avia</p> <p>- Intel Realsense L515 & D455</p> <p>- Microsoft Azure Kinect</p> <p>- Stereolabs ZED2i</p> <p>The groundtruth IMU trajectories (gt.tar.gz) are estimated by a batch LiDAR-IMU optimization that aligns point cloud scans with an environmental map (map_e232b.las) recorded with a survey-grade LiDAR (FARO Focus).</p> <p> </p>
Data from: Preferred gait and walk–run transition speeds in ostriches measured using GPS-IMU sensors
The ostrich (Struthio camelus) is widely appreciated as a fast and agile bipedal athlete, and is a useful comparative bipedal model for human locomotion. Here, we used GPS-IMU sensors to measure naturally selected gait dynamics of ostriches roaming freely over a wide range of speeds in an open field and developed a quantitative method for distinguishing walking and running using accelerometry. We compared freely selected gait–speed distributions with previous laboratory measures of gait dynamics and energetics. We also measured the walk–run and run–walk transition speeds and compared them with those reported for humans. We found that ostriches prefer to walk remarkably slowly, with a narrow walking speed distribution consistent with minimizing cost of transport (CoT) according to a rigid-legged walking model. The dimensionless speeds of the walk–run and run–walk transitions are slower than those observed in humans. Unlike humans, ostriches transition to a run well below the mechanical limit necessitating an aerial phase, as predicted by a compass-gait walking model. When running, ostriches use a broad speed distribution, consistent with previous observations that ostriches are relatively economical runners and have a flat curve for CoT against speed. In contrast, horses exhibit U-shaped curves for CoT against speed, with a narrow speed range within each gait for minimizing CoT. Overall, the gait dynamics of ostriches moving freely over natural terrain are consistent with previous lab-based measures of locomotion. Nonetheless, ostriches, like humans, exhibit a gait-transition hysteresis that is not explained by steady-state locomotor dynamics and energetics. Further study is required to understand the dynamics of gait transitions.
Data from: Preferred gait and walk–run transition speeds in ostriches measured using GPS-IMU sensors
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The Effect of Digital Rehabilitation System With Wearable Multi-IMU (Inertial Measurement Unit) Sensors on Upper Limb Functions in Children With Brain Injury
ClinicalTrials.gov study NCT02949817. IPD Sharing: NO. Countries: 1. Publications: 0.
The Exploratory Study of AR-based Home Rehabilitation Exercise Combined With Wearable Sensor (IMU) in the Elderly
ClinicalTrials.gov study NCT05598463. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.